Bot Academy · Platform overview
We use AI to help teach and develop trading bots. Bot Academy connects bot education, market research and simulation in a repeatable process for testing strategy improvements and building evidence for review.
Current stage: research and simulation
Problem & solution
Evaluating a trading strategy requires connected data, realistic testing and a clear record of changes. Isolated experiments make it harder to compare results and determine what deserves further development.
Bot Academy brings AI-assisted teaching, controlled strategy comparisons and simulator execution into one workflow. Shared infrastructure supports research across markets while preserving each experiment’s identity.
AI-assisted bot education
Our initial AI teaching pilot reviews market evidence, assigns lessons and proposes targeted strategy improvements. The Academy tests those changes and records the results, creating a development history that supports the next review.
AI reviews development evidence, identifies what a bot should observe next or proposes a limited change to a strategy parameter.
Run the proposed strategy version as a separate experiment against defined criteria and cost assumptions.
Assess outcomes against the baseline and preserve the lessons, including inconclusive or rejected changes.
Use the evidence to guide further development. Qualification and trading authorization require separate review.
The teaching pilot starts with selected strategy families. AI-guided development is evaluated through recorded experiments; the teacher does not approve candidates or enable trading.
Platform capabilities
Bot Academy connects strategy development, execution validation, controlled experimentation, AI decision support and market infrastructure.
Bot Academy V17 organizes lessons, strategy versions, market evidence and experiment results across futures, stocks, currencies, crypto, options and commodities.
Value created: a reusable development environment where each strategy retains its identity, evidence and learning history.
Standalone and Morning V4 test futures strategies through NinjaTrader simulation in separate accounts. Morning V4 limits new entries to the morning.
Value created: a way to evaluate signals, protective orders and trade management while keeping execution records distinct.
Reference-versus-candidate tests compare existing and revised rules on identical observations. Roster V3 adds market-evidence checks and a dedicated simulator account; ESv1 provides another separate futures experiment.
Value created: comparable strategy evidence with explicit test assumptions and a clear distinction between modeled and simulator results.
The ES Decision Desk assesses markets and plans in a paper model. The Codex Context Desk maintains a market thesis, watches developing setups and assesses its separate simulator account.
Value created: documented reasoning for acting or waiting, with software checks before authorized execution.
MarketPacket, data collectors and the bar recorder preserve market context and timing. Research fleets, trade records, monitoring and backups support review and recovery.
Value created: infrastructure that can serve multiple experiments and a record of what each system knew, tested and observed.
Evidence & validation
Each stage answers a different question. Research evidence, simulator execution and live-capital readiness are evaluated separately.
Modeled paper results compare strategy rules using recorded observations and defined fill and cost assumptions.
Native NinjaTrader simulator records test order handling, protection and trade management. Simulation does not establish live-market returns.
Review broader market conditions, costs, risk and operational reliability. Graduation and live-capital authorization remain separate decisions.
Risk management & governance
The platform connects AI-assisted education, controlled experiments and a traceable review process.
Current stage & investment considerations
Bot Academy’s investment proposition centers on AI-assisted bot development, reusable market infrastructure and a disciplined evaluation process. The current focus is research and simulation, with an initial AI teaching pilot for selected strategy families.
Progress should be assessed through repeatable results after costs, performance across market conditions, reliable execution and clear accountability. Live-money profitability has not been established.
Explore bot education View research performance